An Inertial Sequence Learning Framework for Vehicle Speed Estimation via Smartphone IMU

TL;DR

This paper presents a novel inertial sequence learning framework that leverages smartphone IMU data and GNSS supervision to accurately estimate vehicle speed, incorporating noise compensation, pose alignment, data augmentation, and a new loss function.

Contribution

The paper introduces a comprehensive velocity estimation framework combining noise modeling, pose alignment, data augmentation, and a novel loss function for improved accuracy and robustness.

Findings

Achieved superior accuracy in vehicle speed estimation compared to existing methods.

Demonstrated the framework's effectiveness on real-world crowdsourcing datasets.

Enhanced model generalizability through innovative data augmentation techniques.

Abstract

Accurately estimating vehicle velocity via smartphone is critical for mobile navigation and transportation. This paper introduces a cutting-edge framework for velocity estimation that incorporates temporal learning models, utilizing Inertial Measurement Unit (IMU) data and is supervised by Global Navigation Satellite System (GNSS) information. The framework employs a noise compensation network to fit the noise distribution between sensor measurements and actual motion, and a pose estimation network to align the coordinate systems of the phone and the vehicle. To enhance the model's generalizability, a data augmentation technique that mimics various phone placements within the car is proposed. Moreover, a new loss function is designed to mitigate timestamp mismatches between GNSS and IMU signals, effectively aligning the signals and improving the velocity estimation accuracy. Finally, we implement a highly efficient prototype and conduct extensive experiments on a real-world crowdsourcing dataset, resulting in superior accuracy and efficiency.